David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In Ruth Hagengruber (ed.), Philosophy's Relevance in Information Science (forthcoming)
Knowledge Representation and Reasoning (KR&R) is based on the idea that propositional content can be rigorously represented in formal languages long the province of logic, in such a way that these representations can be productively reasoned over by humans and machines; and that this reasoning can be used to produce knowledge-based systems (KBSs). As such, KR&R is a discipline conventionally regarded to range across parts of artificial intelligence (AI), computer science, and especially logic. This standard view of KR&R’s participating fields is correct — but dangerously incomplete. The view is incomplete because, as we explain herein, sophisticated KR&R must rely heavily upon philosophy. Encapsulated, the reason is actually quite straightforward: Sophisticated KR&R must include the representation of not only simple properties, but also concepts that are routine in the formal sciences (theoretical computer science, mathematics, logic, game theory, etc.), and everyday socio-cognitive concepts like mendacity, deception, betrayal, and evil. Because in KR&R the representation of such concepts must be rigorous in order to enable machine reasoning (e.g., machine-generated and machine-checked proofs that a is lying to b) over them, philosophy, devoted as it is in no small part to supplying analyses of such concepts, is a crucial partner in the overall enterprise. To put the point another way: When the knowledge to be represented is such as to require lengthy formulas in expressive formal languages for that representation, philosophy must be involved in the game. In addition, insofar as the advance of KR&R must allow formalisms and processes for representing and reasoning over visual propositional content, philosophy will be a key contributor into the future.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Gerhard Heyer (1988). Generic Generalisations, Discourse Representation Structures, and Knowledge Representation. In Jakob Hoepelman (ed.), Representation and Reasoning: Proceedings of the Stuttgart Conference Workshop on Discourse Representation, Dialogue Tableaux, and Logic Programming. M. Niemeyer Verlag.
DrsP A. Smit (1988). Argumentation Theory and Knowledge Representation. In Jakob Hoepelman (ed.), Representation and Reasoning: Proceedings of the Stuttgart Conference Workshop on Discourse Representation, Dialogue Tableaux, and Logic Programming. M. Niemeyer Verlag.
Gerard Allwein & Jon Barwise (eds.) (1996). Logical Reasoning with Diagrams. Oxford University Press.
Mark Eli Kalderon (2001). Reasoning and Representing. Philosophical Studies 105 (2):129-160.
Adam Wyner (2008). An Ontology in Owl for Legal Case-Based Reasoning. Artificial Intelligence and Law 16 (4):361-387.
Brian R. Gaines (2009). Designing Visual Languages for Description Logics. Journal of Logic, Language and Information 18 (2):217-250.
Keith Stenning & Oliver Lemon (1999). Aligning Logical and Psychological Perspectives on Diagrammatic Reasoning. Philosophical Explorations.
Added to index2009-10-23
Total downloads14 ( #130,846 of 1,679,401 )
Recent downloads (6 months)1 ( #183,003 of 1,679,401 )
How can I increase my downloads?